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Umm, StyleGAN was the first decent image generation model, and it was producing great images from random seeds 5 years ago. Now, that's with the obvious caveat that each model was trained to produce one specific type of image and it helped immensely if the training images were all aligned the same. Diffusion models are certainly the trendy current architecture for image generation, but AFAIK there's no fundamental theoretical limitation to the output quality of any architecture except the general rule that more parameters is better.
GANs on the other hand uses a generator and discriminator model, where the generator learns to generate images that are "good enough" to fool the discriminator. It may make it easier to make discriminators that are better at picking out visual artefacts that stand out for humans, (it was easy to pick out "fake humans" from the early versions of thispersondoesnotexist.com by looking at their eyes and teeth, they tend to be pointing in different directions, or their teeth are too irregular before the discriminator got better at picking those out) but they can suffer from mode collapse, and generate images with very little variety.